Mutation analysis is an effective, if computationally expensive, technique that allows practitioners to accurately evaluate the quality of their test suites. To reduce the time and cost of mutation analysis, researchers have looked at parallelizing mutation runs — running multiple mutated versions of the program in parallel, and running through the tests in sequence on each mutated program until a bug is found. While an improvement over sequential execution of mutants and tests, this technique carries a significant overhead cost due to its redundant execution of unchanged code paths. In this paper we propose a novel technique (and its implementation) which parallelizes the test runs rather than the mutants, forking mutants from a single program execution at the point of invocation, which reduces redundancy. We show that our technique can lead to significant efficiency improvements and cost reductions.